Depth-Integrated Estimation of Dissolved Oxygen in a Lake
Publication: Journal of Environmental Engineering
Volume 137, Issue 10
Abstract
The majority of variable estimation studies in water resources investigate the temporal variation of the variable. In this study, we examined the depth-dependent estimation of a lake’s dissolved oxygen (DO) using two artificial neural network (ANN) methods: (1) the radial basis functions (RBFs) and the feed forward back-propagation (FFBP), and (2) the multilinear regression (MLR). We tested two different input layer configurations. In the first case, we employed all other available lake parameters—total dissolved solids (TDS), pH, conductivity, lake depth, and lake temperature—to estimate DO. In the second case, we considered only depth and temperature to estimate DO. The performance evaluation criteria of these two cases were close. ANN estimation performances were noticeably superior to those of MLR, as reflected in the performance evaluation criteria and DO lake depth plots. We saw that the spatial variation of the lake’s DO can be captured by ANNs satisfactorily, even if available measurements are quite limited.
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Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 137 • Issue 10 • October 2011
Pages: 961 - 967
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Jan 3, 2010
Accepted: Feb 11, 2011
Published online: Feb 14, 2011
Published in print: Oct 1, 2011
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